(2026-06) Balanced Additive Randomized Encodings for Shuffle Differential Privacy
2026-06-10
Shuffle differential privacy (shuffle DP) offers an attractive distributed alternative to standard differential privacy. It uses a secure shuffler to permute users' randomized encodings, providing individual data privacy without a central trusted entity. A key challenge, however, is to achieve both generality and client efficiency. Under information-theoretic shuffle-DP guarantees, protocols that nearly match central-model utility are restricted to statistical tasks such as summation and histograms. In contrast, in the computational setting, additive randomized encodings (ARE), introduced by Halevi et al. (CRYPTO 2023), yield a generic compiler that achieves central-model utility for arbitrary mechanisms. However, their construction incurs prohibitive worst-case computation and communication costs for clients, making it impractical for resource-constrained devices such as mobile phones, IoT sensors, and web browsers.
In this work, we present a client-efficient one-round compiler from central DP to non-robust computational shuffle DP. To achieve this, we first construct a new balanced ARE scheme, where all the clients almost equally share the computational burden in the protocol. For a mechanism over clients, this reduces the worst-case per-client computation from in prior work to , where denotes the circuit complexity of the mechanism. The key technical ingredient is a new permute-XOR ARE primitive that enables wire splicing across independently generated garbled subcircuits. Secondly we design a more efficient ARE-to-shuffle compiler, whose client bandwidth scales with the sparsity and cross-partition structure parameter of the ARE encoding - a quantity that is always sublinear in . This is an improvement over Halevi et al. (CRYPTO 2023) where each client bandwidth is . At a high level, the first contribution improves client computation while the second improves client bandwidth.
We provide an implementation of our generic compiler for differentially private tasks including selection, distinct elements, and linear contextual bandits. We obtain shuffle protocols that match the utility of their central-model counterparts with reasonable client overhead. We evaluate our method across a range of practical settings, and observe substantial gains over the compiler of Halevi et al., with per-client computational and bandwidth speedups increasing linearly in the number of clients.